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Fix reorder columns #256

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Jul 24, 2024
Merged

Fix reorder columns #256

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00a2e25
fix order cols
Melkiades Jun 28, 2024
ac13761
[skip style] [skip vbump] Restyle files
github-actions[bot] Jun 28, 2024
36871fc
flamboyant rstudio finalyl
Melkiades Jun 28, 2024
8f22a91
Merge branch '255_fix_column_order@main' of github.com:insightsengine…
Melkiades Jun 28, 2024
6de81b6
Apply suggestions from code review
Melkiades Jun 28, 2024
8520711
Merge branch 'main' into 255_fix_column_order@main
Melkiades Jul 1, 2024
b81170a
fixing snaps
Melkiades Jul 10, 2024
ff8069e
reinstating the rendering
Melkiades Jul 10, 2024
c923bf0
adding snaps for plots
Melkiades Jul 10, 2024
9728b81
case where new folder is needed
Melkiades Jul 10, 2024
87e9fb4
fix minor
Melkiades Jul 10, 2024
669a8e3
Merge branch '255_fix_column_order@main' of github.com:insightsengine…
Melkiades Jul 10, 2024
a47068f
taking out plots again
Melkiades Jul 10, 2024
dfd86b9
removing tmp
Melkiades Jul 10, 2024
3602521
snapshot change will revert?
Melkiades Jul 23, 2024
a3a38ca
final fix?
Melkiades Jul 23, 2024
0159ae9
revert layout changes
pawelru Jul 24, 2024
4543762
refresh ADA
pawelru Jul 24, 2024
db6cb21
refresh snapshot for ratet01
pawelru Jul 24, 2024
427c9bb
exclude rbmit from snapshot testing
pawelru Jul 24, 2024
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39 changes: 27 additions & 12 deletions book/tables/ADA/adat01.qmd
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,12 +20,32 @@ library(tibble)
adsl <- random.cdisc.data::cadsl
adab <- random.cdisc.data::cadab

combodf <- tribble(
~valname, ~label, ~levelcombo, ~exargs,
"all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
"all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
# Order needed for the columns is c(1, 3, 4, 2, 5)
reorder_facets <- function(splret, spl, fulldf, ...) {
ord <- c(1, 3, 4, 2, 5)
make_split_result(
splret$values[ord],
splret$datasplit[ord],
splret$labels[ord]
)
}

# Create a custom split function for adding the new columns (facets) and sorting them
custom_column_split_fun <- make_split_fun(
post = list(
add_combo_facet("all_X",
label = "All Drug X",
levels = c("A: Drug X", "C: Combination")
),
add_combo_facet("all_pt",
label = "All Patients",
levels = c("A: Drug X", "B: Placebo", "C: Combination")
),
reorder_facets
)
)


# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adab <- adab %>%
Expand Down Expand Up @@ -133,12 +153,13 @@ adab_pb <- df_explicit_na(adab) %>%
## {{< fa regular file-lines sm fw >}} Preview

<!-- skip strict because of https://github.com/insightsengineering/rtables/issues/830 -->

```{r variant1, test = list(result_v1 = "result"), opts.label = ifelse(packageVersion("rtables") < "0.6.6.9011", "skip_test_strict", "")}
# Layout for Baseline Prevalence of NAbs
lyt_bl <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(
"ACTARM",
split_fun = add_combo_levels(combodf)
split_fun = custom_column_split_fun
) %>%
count_patients_with_flags(
"USUBJID",
Expand Down Expand Up @@ -169,7 +190,7 @@ lyt_bl <- basic_table(show_colcounts = TRUE) %>%
lyt_pb <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(
"ACTARM",
split_fun = add_combo_levels(combodf)
split_fun = custom_column_split_fun
) %>%
count_patients_with_flags(
"USUBJID",
Expand Down Expand Up @@ -217,12 +238,6 @@ result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
result_1@col_info <- result_2@col_info
result <- rbind(result_1, result_2)

# Change the column order.
result <- cbind_rtables(result[, 1], result[, 3]) %>%
cbind_rtables(result[, 4]) %>%
cbind_rtables(result[, 2]) %>%
cbind_rtables(result[, 5])

main_title(result) <- paste(
"Baseline Prevalence and Incidence of Treatment Emergent ADA"
)
Expand Down
39 changes: 27 additions & 12 deletions book/tables/ADA/adat04a.qmd
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Original file line number Diff line number Diff line change
Expand Up @@ -20,10 +20,30 @@ library(tibble)
adsl <- random.cdisc.data::cadsl
adab <- random.cdisc.data::cadab

combodf <- tribble(
~valname, ~label, ~levelcombo, ~exargs,
"all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
"all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()

# Order needed for the columns is c(1, 3, 4, 2, 5)
reorder_facets <- function(splret, spl, fulldf, ...) {
ord <- c(1, 3, 4, 2, 5)
make_split_result(
splret$values[ord],
splret$datasplit[ord],
splret$labels[ord]
)
}

# Create a custom split function for adding the new columns (facets) and sorting them
custom_column_split_fun <- make_split_fun(
post = list(
add_combo_facet("all_X",
label = "All Drug X",
levels = c("A: Drug X", "C: Combination")
),
add_combo_facet("all_pt",
label = "All Patients",
levels = c("A: Drug X", "B: Placebo", "C: Combination")
),
reorder_facets
)
)

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
Expand Down Expand Up @@ -160,12 +180,13 @@ adab_pb <- df_explicit_na(adab) %>%
## {{< fa regular file-lines sm fw >}} Preview

<!-- skip strict because of https://github.com/insightsengineering/rtables/issues/830 -->

```{r variant1, test = list(result_v1 = "result"), opts.label = ifelse(packageVersion("rtables") < "0.6.6.9011", "skip_test_strict", "")}
# Layout for Baseline Prevalence of NAbs
lyt_bl <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(
"ACTARM",
split_fun = add_combo_levels(combodf)
split_fun = custom_column_split_fun
) %>%
count_patients_with_flags(
"USUBJID",
Expand Down Expand Up @@ -194,7 +215,7 @@ lyt_bl <- basic_table(show_colcounts = TRUE) %>%
lyt_pb <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(
"ACTARM",
split_fun = add_combo_levels(combodf)
split_fun = custom_column_split_fun
) %>%
count_patients_with_flags(
"USUBJID",
Expand Down Expand Up @@ -241,12 +262,6 @@ result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
# Combine tables
result <- rbind(result_1, result_2)

# Change the column order
result <- cbind_rtables(result[, 1], result[, 3]) %>%
cbind_rtables(result[, 4]) %>%
cbind_rtables(result[, 2]) %>%
cbind_rtables(result[, 5])

main_title(result) <- paste(
"Baseline Prevalence and Incidence of Treatment Emergent NAbs"
)
Expand Down
38 changes: 26 additions & 12 deletions book/tables/ADA/adat04b.qmd
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,10 +20,29 @@ library(tibble)
adsl <- random.cdisc.data::cadsl
adab <- random.cdisc.data::cadab

combodf <- tribble(
~valname, ~label, ~levelcombo, ~exargs,
"all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
"all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
# Order needed for the columns is c(1, 3, 4, 2, 5)
reorder_facets <- function(splret, spl, fulldf, ...) {
ord <- c(1, 3, 4, 2, 5)
make_split_result(
splret$values[ord],
splret$datasplit[ord],
splret$labels[ord]
)
}

# Create a custom split function for adding the new columns (facets) and sorting them
custom_column_split_fun <- make_split_fun(
post = list(
add_combo_facet("all_X",
label = "All Drug X",
levels = c("A: Drug X", "C: Combination")
),
add_combo_facet("all_pt",
label = "All Patients",
levels = c("A: Drug X", "B: Placebo", "C: Combination")
),
reorder_facets
)
)

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
Expand Down Expand Up @@ -140,12 +159,13 @@ adab_pb <- left_join(adab_pb_ada, adab_pb_adap, by = mergecol) %>%
## {{< fa regular file-lines sm fw >}} Preview

<!-- skip strict because of https://github.com/insightsengineering/rtables/issues/830 -->

```{r variant1, test = list(result_v1 = "result"), opts.label = ifelse(packageVersion("rtables") < "0.6.6.9011", "skip_test_strict", "")}
# Layout for Baseline Prevalence of NAbs
lyt_bl <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(
"ACTARM",
split_fun = add_combo_levels(combodf)
split_fun = custom_column_split_fun
) %>%
count_patients_with_flags(
"USUBJID",
Expand Down Expand Up @@ -175,7 +195,7 @@ lyt_bl <- basic_table(show_colcounts = TRUE) %>%
lyt_pb <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(
"ACTARM",
split_fun = add_combo_levels(combodf)
split_fun = custom_column_split_fun
) %>%
count_patients_with_flags(
"USUBJID",
Expand Down Expand Up @@ -207,12 +227,6 @@ result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
# Combine tables.
result <- rbind(result_1, result_2)

# Change the column order.
result <- cbind_rtables(result[, 1], result[, 3]) %>%
cbind_rtables(result[, 4]) %>%
cbind_rtables(result[, 2]) %>%
cbind_rtables(result[, 5])

main_title(result) <- paste(
"Baseline Prevalence and Incidence of Neutralizing Antibodies (NAbs)"
)
Expand Down
9 changes: 6 additions & 3 deletions book/tables/efficacy/rbmit01.qmd
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@ subtitle: Tables for RBMI

{{< include ../../_utils/envir_hook.qmd >}}

:::: {.panel-tabset}
::: panel-tabset
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## Data Setup

We use a publicly available example dataset from an antidepressant clinical trial of an active drug versus placebo from the `rbmi` package.
Expand Down Expand Up @@ -117,10 +117,11 @@ draws_vars$subjid <- "TMP_ID"
Define which imputation method to use, then create samples for the imputation parameters by running the `draws()` function.

<!-- skip strict because of https://github.com/insightsengineering/rbmi/issues/409 -->

```{r, warning = FALSE, opts.label = "skip_test_strict"}
#| code-fold: show

draws_method <- method_bayes()
draws_method <- method_bayes(seed = 123)

draws_obj <- rbmi::draws(
data = data_full,
Expand Down Expand Up @@ -154,6 +155,7 @@ The next step is to run the analysis model on each imputed dataset.
This is done by defining an analysis function and then calling `rbmi::analyse()` to apply this function to each imputed dataset.

<!-- skip strict because of https://github.com/insightsengineering/rbmi/issues/409 -->

```{r, opts.label = "skip_test_strict"}
#| code-fold: show
#| opts_label: "skip_test_strict" #
Expand Down Expand Up @@ -183,6 +185,7 @@ The `rbmi::pool()` function can be used to summarize the analysis results across
Using the `broom::tidy()` function the `rbmi` final results are reshaped.

<!-- skip strict because of https://github.com/insightsengineering/rbmi/issues/409 -->

```{r, opts.label = "skip_test_strict"}
#| code-fold: show

Expand Down Expand Up @@ -215,5 +218,5 @@ result
{{< include ../../_utils/save_results.qmd >}}

{{< include ../../repro.qmd >}}
:::

::::
32 changes: 16 additions & 16 deletions package/tests/testthat/_snaps/development/tables-ADA-adat01.md
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -5,22 +5,22 @@
Output
Baseline Prevalence and Incidence of Treatment Emergent ADA

—————————————————————————————————————————————————————————————————————————————————————————————————————————————————
A: Drug X C: Combination <Missing> B: Placebo All Drug X
(N=134) (N=132) (N=0) (N=134) (N=266)
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Baseline Prevalence of ADAs
Baseline evaluable patients 134 132 0 0 266
Patient with a positive sample at baseline 63 (47.0%) 64 (48.5%) 0 0 127 (47.7%)
Patient with no positive samples at baseline 71 68 0 0 139
Incidence of Treatment Emergent ADAs
Post-baseline evaluable patients 134 132 0 0 266
Patient positive for Treatment Emergent ADA 0 0 0 0 0
Treatment-induced ADA 0 0 0 0 0
Treatment-enhanced ADA 0 0 0 0 0
Patient negative for Treatment Emergent ADA 0 0 0 0 0
Treatment unaffected 0 0 0 0 0
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————
A: Drug X C: Combination <Missing> B: Placebo All Drug X
(N=134) (N=132) (N=0) (N=134) (N=266)
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Baseline Prevalence of ADAs
Baseline evaluable patients 134 132 0 0 266
Patient with a positive sample at baseline 63 (47.0%) 64 (48.5%) 0 0 127 (47.7%)
Patient with no positive samples at baseline 71 68 0 0 139
Incidence of Treatment Emergent ADAs
Post-baseline evaluable patients 134 132 0 0 266
Patient positive for Treatment Emergent ADA 0 0 0 0 0
Treatment-induced ADA 0 0 0 0 0
Treatment-enhanced ADA 0 0 0 0 0
Patient negative for Treatment Emergent ADA 0 0 0 0 0
Treatment unaffected 0 0 0 0 0
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————

ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient = a patient with an ADA assay result from a baseline sample(s)
Post-baseline evaluable patient = a patient with an ADA assay result from at least one post-baseline sample Number of patients positive for Treatment Emergent
Expand Down
36 changes: 18 additions & 18 deletions package/tests/testthat/_snaps/development/tables-efficacy-rbmit01.md
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Original file line number Diff line number Diff line change
Expand Up @@ -13,24 +13,24 @@
Relative Reduction (%) 5.7%
p-value (RBMI) 0.8932
5
Adjusted Mean (SE) -4.251 (0.647) -2.805 (0.644)
95% CI (-5.528, -2.974) (-4.077, -1.533)
Difference in Adjusted Means (SE) 1.446 (0.914)
95% CI (-0.358, 3.250)
Relative Reduction (%) -34.0%
p-value (RBMI) 0.1155
Adjusted Mean (SE) -4.255 (0.653) -2.857 (0.634)
95% CI (-5.546, -2.965) (-4.109, -1.605)
Difference in Adjusted Means (SE) 1.398 (0.915)
95% CI (-0.409, 3.205)
Relative Reduction (%) -32.9%
p-value (RBMI) 0.1285
6
Adjusted Mean (SE) -6.366 (0.721) -4.095 (0.696)
95% CI (-7.793, -4.939) (-5.471, -2.720)
Difference in Adjusted Means (SE) 2.270 (0.996)
95% CI (0.301, 4.239)
Relative Reduction (%) -35.7%
p-value (RBMI) 0.0241
Adjusted Mean (SE) -6.413 (0.712) -4.166 (0.692)
95% CI (-7.821, -5.005) (-5.533, -2.800)
Difference in Adjusted Means (SE) 2.246 (1.005)
95% CI (0.260, 4.232)
Relative Reduction (%) -35.0%
p-value (RBMI) 0.0269
7
Adjusted Mean (SE) -7.689 (0.797) -4.776 (0.752)
95% CI (-9.266, -6.112) (-6.261, -3.290)
Difference in Adjusted Means (SE) 2.913 (1.073)
95% CI (0.792, 5.035)
Relative Reduction (%) -37.9%
p-value (RBMI) 0.0074
Adjusted Mean (SE) -7.652 (0.775) -4.939 (0.771)
95% CI (-9.185, -6.120) (-6.465, -3.414)
Difference in Adjusted Means (SE) 2.713 (1.099)
95% CI (0.538, 4.888)
Relative Reduction (%) -35.5%
p-value (RBMI) 0.0149

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